{"id":727,"date":"2026-05-23T05:05:05","date_gmt":"2026-05-23T05:05:05","guid":{"rendered":"https:\/\/listenlabs.ai\/articles\/dovetail-alternative-discussio-2026\/"},"modified":"2026-08-07T05:16:12","modified_gmt":"2026-08-07T05:16:12","slug":"dovetail-alternative-discussio-2026","status":"publish","type":"post","link":"https:\/\/listenlabs.com\/articles\/dovetail-alternative-discussio-2026\/","title":{"rendered":"Dovetail Alternative to Discuss.io: Full-Lifecycle Research"},"content":{"rendered":"<p><em>Written by: Anish Rao, Head of Growth, Listen Labs | Last updated: August 6, 2026<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways for Research Leaders<\/h2>\n<ul>\n<li>Enterprise teams stitching Discuss.io and Dovetail absorb real operational costs from manual handoffs, context loss, and duplicated setup work.<\/li>\n<li>Unified AI platforms bring study design, recruitment, moderation, analysis, and knowledge management into one environment, removing the seam between collection and repository tools.<\/li>\n<li>Listen Labs integrates recruitment through Listen Atlas, real-time Quality Guard monitoring, AI moderation in 100+ languages, and automated deliverables to compress research cycles from weeks to under 24 hours.<\/li>\n<li>Teams gain statistical confidence through mixed-method studies, emotional intelligence capture, and cross-study queries without manual tagging or context recreation.<\/li>\n<li>Listen Labs replaces both collection and analysis tools in one platform. <a href=\"https:\/\/listenlabs.com\/book-my-demo\" target=\"_blank\"><strong>Evaluate whether it fits your team\u2019s full research lifecycle.<\/strong><\/a><\/li>\n<\/ul>\n<h2>Study Setup and Design in One Workspace<\/h2>\n<p>Fragmented stacks force study design to hop between tools. Work often starts in a document editor, moves into Discuss.io for interview guide configuration, then shifts again into Dovetail to prepare tagging taxonomies and analysis templates before a single interview runs. Each environment needs separate configuration, and late guide changes must be replicated manually across systems.<\/p>\n<p>Unified AI platforms keep study co-design in a single workspace. <a href=\"https:\/\/listenlabs.com\/blog\/what-is-qual-at-scale\" target=\"_blank\">AI can schedule and conduct interviews, analyze transcripts for themes, and generate quantitative insights from those interviews<\/a>, all from one configuration. Listen Labs eliminates duplicate setup by supporting branching logic, skip logic, stimuli upload (images, video, PDFs, live URLs), monadic and sequential randomization, and auto-QA in one place. A study brief entered in natural language becomes a structured discussion guide, recruitment criteria, and analysis scaffolding at the same time.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.com\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773098461736-796a7724447a.png\" alt=\"Screenshot of researcher creating a study by simply typing &quot;I want to interview Gen Z on how they use ChatGPT&quot;\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Our AI helps you go from idea to implemented discussion guide in seconds.<\/em><\/figcaption><\/figure>\n<h2>Recruitment and Sampling Without Extra Vendors<\/h2>\n<p>Discuss.io does not include a proprietary participant panel. Teams using it source participants through separate recruitment vendors such as User Interviews or Respondent, then schedule them into Discuss.io sessions. <a href=\"https:\/\/conveo.ai\/insights\/user-interview-recruitment\" target=\"_blank\" rel=\"noindex nofollow\">Most teams manage at least three separate platforms to conduct user interviews: one for recruitment, one for scheduling and session management, and another for transcription and analysis.<\/a> Dovetail sits downstream of this stack and has no recruitment function.<\/p>\n<p>The recruitment bottleneck is significant. Recruiting ten sessions can require three to five hours of scheduling logistics, and 15\u201325% of confirmed participants may not attend. Replacement recruitment often extends timelines to two to four weeks for a panel of ten qualified participants.<\/p>\n<p>Listen Labs integrates recruitment directly through Listen Atlas, a global network of 30M verified respondents across 45+ countries and 100+ languages. An AI orchestration layer matches participants using behavioral and intent signals, not just self-reported demographics. A dedicated recruitment operations team handles niche segments such as enterprise decision-makers, healthcare workers, and audiences below 1% incidence rate that commodity panels rarely reach. Participant frequency is capped at three studies per month per respondent, which removes professional survey-takers.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.com\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773098685817-eaceb6089d9a.png\" alt=\"Listen Labs finds participants and helps build screener questions\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Listen Labs finds participants and helps build screener questions<\/em><\/figcaption><\/figure>\n<p><a href=\"https:\/\/listenlabs.com\/book-my-demo\" target=\"_blank\"><strong>See how Listen Atlas handles your specific audience requirements.<\/strong><\/a><\/p>\n<h2>Moderation Approach: AI Scale with Human Flexibility<\/h2>\n<p>Discuss.io centers on live human moderation. A trained moderator conducts sessions in real time, which supports strong rapport and adaptability but creates throughput ceilings. <a href=\"https:\/\/koji.so\/docs\/ai-vs-human-moderators\" target=\"_blank\" rel=\"noindex nofollow\">Human moderators are limited to 4\u20136 interviews per day due to fatigue, while AI moderators can handle an unlimited number per day with consistent probe wording and full guide coverage.<\/a><\/p>\n<p>AI-moderated interviews now support most well-defined research questions. <a href=\"https:\/\/cleverx.com\/blog\/ai-vs-human-moderated-interviews-in-2026-when-to-use-which-and-why-most-teams-need-both\" target=\"_blank\" rel=\"noindex nofollow\">By 2026, AI-moderated interviews support multilingual research across 50+ languages with consistent quality, while human moderators remain constrained by individual language skills.<\/a> Listen Labs runs AI-led video interviews with dynamic follow-up questions and collects video, audio, text, and screen recordings, including mobile iOS. The platform supports 100+ languages for interview moderation.<\/p>\n<p>Human moderation still excels on emotionally charged topics, deeply exploratory work where the goal is to find the right questions, and executive-level interviews where senior participants prefer peer-to-peer conversation. <a href=\"https:\/\/greenbook.org\/insights\/the-prompt-ai\/ai-moderation-in-market-research-when-its-good-enough-and-when-judgment-matters-more\" target=\"_blank\" rel=\"noindex nofollow\">Higher emotional or strategic risk justifies more human involvement in qualitative moderation.<\/a> A practical 2026 model uses a hybrid approach: AI moderation for breadth at volume, human moderation for high-stakes strategic depth.<\/p>\n<h2>Data Quality Controls Across the Full Journey<\/h2>\n<p>Quality assurance in a fragmented stack remains mostly manual. Screening happens at the recruitment vendor. Session quality is assessed by the human moderator during Discuss.io sessions. Data integrity in Dovetail depends on what the moderator chose to tag and upload. No single quality signal spans the full participant journey.<\/p>\n<p>Listen Labs\u2019 Quality Guard monitors every interview in real time across video, voice, content, and device signals to detect fraud, low-effort responses, AI-generated scripts, and mismatched profiles. Reputation scoring compounds across every interview on the platform, which steadily strengthens audience quality. These controls support richer responses and more complete guide coverage because low-quality participants are identified and removed early.<\/p>\n<h2>Qualitative Depth with Quantitative Confidence<\/h2>\n<p>Sample size is a structural limitation of the Discuss.io and Dovetail combination. Live human moderation caps throughput at roughly 4\u20136 sessions per moderator per day, so a 30-interview study requires at least a week of fieldwork. <a href=\"https:\/\/listenlabs.com\/blog\/what-is-qual-at-scale\" target=\"_blank\">With qual-at-scale, the old trade-off between depth and scale no longer blocks decision-making.<\/a><\/p>\n<p>Recent industry reports show that median qualitative sample sizes for AI-moderated studies have grown sharply. Listen Labs supports mixed-method studies that combine open-ended conversational questions with Likert scales, NPS, sliders, grids, and MaxDiff in a single interview. Teams no longer need separate quantitative surveys to reach statistical confidence.<\/p>\n<h2>Analysis Workflow and Deliverable Creation<\/h2>\n<p>In the Discuss.io and Dovetail stack, analysis starts only after fieldwork closes. Recordings from Discuss.io must be exported, transcribed, and imported into Dovetail. Researchers then apply tags manually, build affinity diagrams, and write synthesis. <a href=\"https:\/\/qualz.ai\/blog\/research-operations-stack-2026-tool-sprawl\" target=\"_blank\" rel=\"noindex nofollow\">Context recreation alone accounts for 20\u201325% of project time in fragmented stacks.<\/a><\/p>\n<p><a href=\"https:\/\/listenlabs.com\/blog\/research-agent\" target=\"_blank\">Listen Labs\u2019 Research Agent handles the full analysis workflow from raw data to final output<\/a>, with every insight linked directly to the underlying response data. One-click deliverables include slide decks in branded templates, memo-style reports, video highlight reels, statistical charts, and segmentation breakdowns. <a href=\"https:\/\/getperspective.ai\/blog\/2026-ai-research-productivity-report-time-to-insight-cut-84-percent\" target=\"_blank\" rel=\"noindex nofollow\">The largest 2026 time reductions on AI research tools occurred in analysis (down 91%) and interviewing (down 81%), with recruiting down 73% and reporting down 67%.<\/a><\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.com\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773098910279-d16bc544a32e.png\" alt=\"Listen Labs auto-generates research reports in under a minute\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Listen Labs auto-generates research reports in under a minute<\/em><\/figcaption><\/figure>\n<p>Listen Labs also captures emotional signals that transcripts alone miss. <a href=\"https:\/\/listenlabs.com\/blog\/emotional-intelligence\" target=\"_blank\">Emotional Intelligence analyzes three signals: tone of voice, word choice, and subconscious micro expressions. Every emotion is quantified per question and concept, and every label is traceable to the exact timestamp, verbatim quote, and AI reasoning behind it.<\/a> This capability connects directly with the Research Agent for natural-language queries and highlight reel generation.<\/p>\n<h2>Cross-Study Knowledge Management with Mission Control<\/h2>\n<p>Dovetail\u2019s core value lies in repository and knowledge management. It organizes past research so teams can retrieve findings without re-reading full reports. This addresses a real gap in many enterprise research operations. Traditional insights teams often leave findings in unsearchable PDFs and slide decks, which accelerates institutional knowledge decay and hides cross-study patterns when team members depart.<\/p>\n<p>Listen Labs\u2019 Mission Control provides the same repository function as a native part of the end-to-end platform. Every study conducted on the platform automatically grows the knowledge base. Teams can run cross-study queries in natural language, track customer sentiment over time, and retrieve sourced answers from past research in seconds. Many enterprise insights teams struggle to access studies older than 12 months. Mission Control addresses this structurally by tying every asset to the original study context rather than relying on manual tagging discipline.<\/p>\n<h2>Operational Burden of Managing Two Vendors<\/h2>\n<p>The operational cost of maintaining separate collection and analysis tools often gets underestimated during vendor selection. <a href=\"https:\/\/qualz.ai\/blog\/research-operations-stack-2026-tool-sprawl\" target=\"_blank\" rel=\"noindex nofollow\">The average enterprise research team in 2026 uses 7\u201312 specialized tools across recruitment, scheduling, interviewing, transcription, analysis, and repository management. Time from research question to actionable insight has not meaningfully decreased for most teams and has increased in many organizations due to tool sprawl.<\/a><\/p>\n<p>Each boundary between Discuss.io and Dovetail introduces a handoff that includes data export, format conversion, context loss, and manual re-entry. <a href=\"https:\/\/qualz.ai\/blog\/research-operations-stack-2026-tool-sprawl\" target=\"_blank\" rel=\"noindex nofollow\">Context stripping at every tool boundary separates findings from supporting transcripts, participant metadata, session dynamics, and researcher notes by the time data reaches the repository.<\/a> Dovetail receives a degraded version of the original research context.<\/p>\n<p>Vendor management overhead compounds this burden. Teams manage two contracts, two renewal cycles, two security reviews, two sets of user permissions, and two support relationships. Maintaining separate tools for recruiting, conducting, transcription, and analysis increases operational costs for a typical product or insights team.<\/p>\n<p><a href=\"https:\/\/listenlabs.com\/book-my-demo\" target=\"_blank\"><strong>Eliminate the collection-to-analysis handoff and see the unified workflow in action.<\/strong><\/a><\/p>\n<h2>Scenario-Based Best-Fit Guidance<\/h2>\n<p>Different team profiles have different tolerance for tool complexity and different research velocity requirements.<\/p>\n<ul>\n<li><strong>Enterprise consumer insights leaders (VP\/Director level at Fortune 500 companies in CPG, retail, tech, or food and beverage):<\/strong> Teams running 4\u20136 week cycles and managing a growing backlog of internal requests benefit most from a unified platform. The Microsoft team collected global customer stories for the company\u2019s 50th anniversary within a day using Listen Labs. Anthropic\u2019s Claude Code team ran 300+ user interviews in 48 hours to surface churn drivers five times faster than previous methods.<\/li>\n<li><strong>UX research leads at mid-to-large product companies:<\/strong> Teams that need to keep pace with sprint cycles and test with 50\u2013100+ users instead of 5\u201310 gain the most from AI moderation at scale. <a href=\"https:\/\/listenlabs.com\/blog\/ai-interviews-beat-focus-groups\" target=\"_blank\">Switching to AI-moderated interviews let Chubbies capture hundreds of candid, one-to-one conversations overnight.<\/a><\/li>\n<li><strong>Product managers and marketing leaders without dedicated research teams:<\/strong> Self-serve study design in natural language, combined with automated recruitment, moderation, and analysis, removes the methodology expertise barrier.<\/li>\n<li><strong>Agencies and consultancies:<\/strong> Client timelines measured in days, along with the need to reach niche audiences globally, favor platforms with integrated recruitment operations and multilingual support. P&amp;G used Listen Labs to deliver 250+ interviews with quantified themes and verbatim proof in hours, directly shaping product and brand strategy.<\/li>\n<\/ul>\n<h2>Risks and Limitations to Weigh<\/h2>\n<p>Unified AI platforms introduce trade-offs that teams should examine before consolidating vendors.<\/p>\n<ul>\n<li><strong>Automation quality assumptions:<\/strong> AI moderation performs well on structured, stimulus-led objectives such as concept testing, message evaluation, brand research, and usability testing. It underperforms on deeply exploratory research where the goal is to discover the right questions, and on emotionally complex or trauma-adjacent topics where human rapport is essential.<\/li>\n<li><strong>Recruitment complexity for niche B2B audiences:<\/strong> Recruiting for a B2B product-market-fit study can require substantial incentives when using separate marketplace tools. Integrated platforms with dedicated recruitment operations reduce this burden, but niche B2B audiences such as Director-level executives and highly specialized professionals still carry higher per-complete costs than general consumer studies.<\/li>\n<li><strong>Speed does not automatically equal quality:<\/strong> <a href=\"https:\/\/impactmr.com\/2026\/02\/16\/ai-in-qualitative-research-where-it-genuinely-adds-value-and-where-humans-still-win\/amp\" target=\"_blank\" rel=\"noindex nofollow\">AI reliably improves speed and consistency in qualitative research and improves quality only when methodologically well set up and strongly governed by humans.<\/a> Teams that bolt AI onto 2019 workflows without restructuring around continuous discovery achieve only 28\u201335% improvement instead of the full gains.<\/li>\n<li><strong>Executive interview acceptance rates:<\/strong> For executive or C-suite interviews, AI-moderated sessions may see higher decline rates than human-moderated sessions because senior B2B participants often prefer peer-to-peer human conversation.<\/li>\n<\/ul>\n<h2>Decision Framework for Choosing a Stack<\/h2>\n<p>Teams can use the following criteria to match their situation to the right approach.<\/p>\n<ul>\n<li><strong>Research cycle time requirement:<\/strong> If insights are needed in under 48 hours to inform a product or campaign decision, a unified AI platform is the only viable option because human moderators cannot reach the required sample size within that window. If a 4\u20136 week cycle is acceptable and the research question is highly exploratory or emotionally sensitive, a human-moderated approach with a separate repository may be appropriate because these studies benefit from human rapport and judgment.<\/li>\n<li><strong>Sample size and statistical confidence:<\/strong> Studies requiring 50+ interviews to reach thematic saturation or support segmentation analysis are impractical with live human moderation alone. AI moderation enables sample sizes of 100\u2013500+ within the same budget envelope.<\/li>\n<li><strong>Audience accessibility:<\/strong> General consumer and mid-level professional audiences are well served by integrated AI recruitment. C-suite and highly specialized niche audiences benefit from platforms with dedicated recruitment operations teams rather than self-serve panel access.<\/li>\n<li><strong>Multilingual and global scope:<\/strong> Demand for multilingual qualitative studies among multinationals continues to grow. Platforms supporting 100+ languages with automatic translation remove the need to coordinate local moderators across markets.<\/li>\n<li><strong>Enterprise security requirements:<\/strong> Any platform replacing two enterprise vendor contracts must meet SOC 2 Type II, GDPR, ISO 27001, ISO 27701, and ISO 42001 standards. Listen Labs holds all five certifications, uses 256-bit encryption, and maintains a policy that customer data is never used for AI model training.<\/li>\n<li><strong>Internal research team capacity:<\/strong> Teams of 1\u20135 researchers that spend 40\u201360% of project time on recruitment coordination rather than analysis gain the most from consolidation. Teams using AI-moderated platforms can often complete more studies per year than traditional teams managing fragmented tools.<\/li>\n<\/ul>\n<h2>Frequently Asked Questions<\/h2>\n<h3>What is the typical turnaround time difference between separate tools and a unified platform?<\/h3>\n<p>A traditional qualitative study using separate recruitment, moderation, and analysis tools often takes several weeks end-to-end. A unified AI platform like Listen Labs compresses the entire cycle to under 24 hours. The largest time reductions occur in analysis and interviewing, where AI automation removes the sequential bottlenecks of human scheduling and manual thematic coding. Teams that restructure around continuous discovery achieve the full reduction, while teams that keep legacy approval and handoff processes typically see 28\u201335% improvement.<\/p>\n<h3>How do unified platforms handle participant sourcing and sample quality compared with stitching Discuss.io and Dovetail?<\/h3>\n<p>As discussed in the Recruitment and Sampling section, Discuss.io relies on separate vendors for participant sourcing and manual scheduling, which introduces no-show risk, scheduling overhead, and a quality assurance gap between screening and the interview. Listen Labs integrates recruitment directly through Listen Atlas, a 30M-respondent network with AI-driven behavioral matching, real-time Quality Guard monitoring, and a dedicated recruitment operations team for hard-to-reach segments. Participant frequency limits of three studies per month per respondent reduce professional survey-takers. Dovetail has no recruitment function and sits entirely downstream of whatever quality the collection tool and its sourcing partners deliver.<\/p>\n<h3>What are the key differences in moderation and emotional intelligence capture?<\/h3>\n<p>Discuss.io uses live human moderators, which supports strong rapport and real-time adaptability but limits daily throughput as described earlier and introduces interviewer variability across a study. AI moderation on Listen Labs conducts unlimited parallel sessions with consistent probe wording, full discussion guide coverage, and dynamic follow-up questions calibrated to each participant\u2019s responses. Beyond spoken content, Listen Labs\u2019 Emotional Intelligence feature analyzes tone of voice, word choice, and subconscious micro expressions to surface emotions that transcripts alone miss, built on Ekman\u2019s universal emotions framework and available across 50+ languages. Every emotional label is traceable to the exact timestamp, verbatim quote, and AI reasoning. Neither Discuss.io nor Dovetail offers this multimodal emotional signal capture as a native, integrated capability.<\/p>\n<h3>How do analysis effort and deliverable speed compare across approaches?<\/h3>\n<p>In the Discuss.io and Dovetail stack, analysis begins after fieldwork closes. Recordings must be exported from Discuss.io, transcribed, imported into Dovetail, and then manually tagged and synthesized by researchers. Context recreation, which re-establishes the relationship between participant metadata, session dynamics, and findings, can account for 20\u201325% of total project time in fragmented stacks. Listen Labs\u2019 Research Agent automates the full analysis workflow from raw interview data to final deliverables, generating slide decks in branded templates, memo-style reports, video highlight reels, statistical charts, and segmentation breakdowns. Every insight links directly to the underlying response data, which maintains traceability without manual tagging. Deliverables are generated in under a minute instead of days of analyst time.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.com\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773099063654-7132de546a42.png\" alt=\"Listen Labs&apos; Research Agent quickly generates consultant-quality PowerPoint slide decks\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Listen Labs&#039; Research Agent quickly generates consultant-quality PowerPoint slide decks<\/em><\/figcaption><\/figure>\n<h3>How do platforms support multilingual research and enterprise security certifications in 2026?<\/h3>\n<p>Listen Labs supports 100+ languages for interview moderation with automatic translation and transcription, which enables simultaneous global studies without coordinating local moderators or separate translation vendors. Discuss.io supports multilingual sessions but relies on human moderators with individual language skills, which limits true parallel global execution. Dovetail supports multiple languages for repository content but has no moderation or recruitment function. On security, Listen Labs holds SOC 2 Type II, GDPR, ISO 27001, ISO 27701, and ISO 42001 certifications, uses 256-bit encryption, and maintains a firm policy that customer data is never used for AI model training. Teams replacing two vendor contracts with one platform consolidate their security review surface area.<\/p>\n<h3>What is the implementation complexity and scalability when replacing two vendors with one platform?<\/h3>\n<p>Replacing Discuss.io and Dovetail with a unified platform removes two contract cycles, two security reviews, two permission management systems, and the integration maintenance between them. The primary implementation consideration is migrating existing research assets such as past studies, tags, and reports from Dovetail into the new repository. Listen Labs\u2019 Mission Control becomes the organization\u2019s source of truth for all past and future research, supporting cross-study queries and trend tracking natively. Scalability is a structural advantage of unified platforms, and teams using AI-moderated platforms can often complete more studies per year than traditional teams managing fragmented tools. Enterprise SSO is supported for access management at scale.<\/p>\n<h2>Conclusion: Matching Tools to Research Goals<\/h2>\n<p>The Discuss.io and Dovetail combination solves two real problems, live moderated interview collection and post-study analysis, but solves them in separate systems that create a measurable operational seam. For teams where research cycle time, sample scale, multilingual reach, and institutional knowledge retention are primary constraints, that seam becomes a compounding cost in time, budget, and data fidelity.<\/p>\n<p>Unified AI research platforms remove the handoff by handling study design, participant sourcing, moderation, analysis, and knowledge management in a single environment. AI moderation is not the right tool for every research question, since deeply exploratory work, emotionally sensitive topics, and executive-level interviews still benefit from human moderators. A 2026 best-practice model uses AI moderation for breadth and volume, human moderation for high-stakes strategic depth, and a unified platform as the shared repository and analysis layer for both.<\/p>\n<p>For enterprise insights teams evaluating whether a single platform can replace both collection and analysis tools, the most relevant criteria include research cycle time, participant quality controls, emotional signal capture, deliverable automation, cross-study knowledge retention, multilingual support, and enterprise security certifications. Listen Labs is built to meet all of these within one platform and is trusted by Microsoft, Anthropic, P&amp;G, Skims, Robinhood, and Nestl\u00e9.<\/p>\n<p><a href=\"https:\/\/listenlabs.com\/book-my-demo\" target=\"_blank\"><strong>Evaluate whether Listen Labs fits your full research lifecycle.<\/strong><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Stop stitching Discuss.io &amp; Dovetail together. Listen Labs unifies recruitment, AI moderation, and analysis in one platform. See it in action.<\/p>\n","protected":false},"author":52,"featured_media":726,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-727","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/posts\/727","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/comments?post=727"}],"version-history":[{"count":1,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/posts\/727\/revisions"}],"predecessor-version":[{"id":1461,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/posts\/727\/revisions\/1461"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/media\/726"}],"wp:attachment":[{"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/media?parent=727"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/categories?post=727"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/tags?post=727"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}